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Ron Perline

dblp:178/7693 · also Ronald K. Perline · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Graphics, computer vision, multimedia, augmented reality and games · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
2 papers
Computational photography and imaging · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational photography and imaging › omnidirectional camera
catadioptric sensor design
0.122004
The Method of Vector Fields for Catadioptric Sensor Design with Applications to Panoramic Imaging · CVPR (2) 2004
Geometric Distributions for Catadioptric Sensor Design · CVPR (1) 2001
Computational photography and imaging
omnidirectional imaging
0.122004
The Method of Vector Fields for Catadioptric Sensor Design with Applications to Panoramic Imaging · CVPR (2) 2004
Geometric Distributions for Catadioptric Sensor Design · CVPR (1) 2001
Computational photography and imaging
panoramic imaging
0.122004
The Method of Vector Fields for Catadioptric Sensor Design with Applications to Panoramic Imaging · CVPR (2) 2004
Geometric Distributions for Catadioptric Sensor Design · CVPR (1) 2001

Methods — techniques the papers use, named apart from their topics

partial differential equations · 0.0least squares approximation · 0.0geometric distributions · 0.0
YearPublicationVenuePosition
2004 The Method of Vector Fields for Catadioptric Sensor Design with Applications to Panoramic Imaging
R. Andrew Hicks, Ron Perline
CVPR (2)2
2001 Geometric Distributions for Catadioptric Sensor Design
abstract
Catadioptric sensors are visual sensors that employ lenses (dioptrics) and mirrors (catoptrics). We present a general method of catadioptric sensor design for realizing prescribed projections. Our method makes use of geometric distributions in. 3-dimensional space, which are generalizations of vector fields. The main idea is this: if one desires a reflective surface that will image the world in a certain way, then this condition determines the orientation of the tangent planes to the surface. Analytically, this means that the surface will then be determined by a pair of partial differential equations, which may or may not have a common solution. We show how to check if a common solution exists. If no common solution exists, we describe a method for obtaining optimal approximate solutions in a least-squares sense. As an example application, we construct a mirror that will give a panoramic view of a scene without any digital unwarping.
R. Andrew Hicks, Ron Perline
CVPR (1)2